Previous work on generating synthetic atmospheric turbulence on images mainly focuses on the visible spectrum. In [1]}, generative adversarial networks (GANs) are utilized for turbulent image generation in the visible spectrum, where sufficiently large-scale data can easily be collected. [2]} uses neural networks to simulate a geometric model on visible band images. In [3]}, [4]}, physics-based models are proposed. There are recent studies that focus on the effects of atmospheric turbulence on long-range observation systems for limited applications fields [5]}. Nevertheless, none of the aforementioned literature propose solutions to turbulence related issues of infrared computer vision problems.
